In:
Journal of Artificial intelligence and Machine Learning, Sciforce LLC, Vol. 1, No. 1 ( 2021-03-19), p. 1-9
Abstract:
Gliomas are the most common central nervous system tumors exhibiting poor survival, quality of life and neurological outcomes prompting significant discussion surrounding optimisation of the aggressiveness of management. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Previous attempts at predicting survival outcomes have relied on clinical parameters (age, KPS, gender) and resection or methylation status and statistical models to create prognostic groups limiting survival prediction due to selection bias and tumor heterogeneity. Machine learning (ML) allows for more sophisticated approaches to survival prediction amalgamating real world clinical, molecular and imaging data. We wanted to examine clinical parameters needed to achieve superior predictive accuracy in order to help advance guidelines for the creation and maintenance of robust large-scale glioma registries.
Type of Medium:
Online Resource
ISSN:
2995-2336
DOI:
10.55124/2831-4905/1/1/
DOI:
10.55124/jaim.v1i1.33
Language:
Unknown
Publisher:
Sciforce LLC
Publication Date:
2021